Data Analysis of NSLDS

Data Analysis: Assumptions

In our dataset, there are similar kinds of tables with different titles. The main structure of those tables contains data of loans by years, quarters and some specific situations which we’ll examine in the next parts. According to our dataset, there will be two types of variables. First one is the constant variable.

While creating charts and applying visualization method between all these variables, some of them must be stay still to see the effect of only one variable. Online cloud data analytics software is useful at this point to determine consumer data analytics in a business.

The second variable is changing variable, which will be the key variable of that chart because only it will have effect on the chart. I believe bar charts will be the key chart of visualization process; which is also an ibm predictive analytics tool to measure customer data analytics.

Also pie chart is one of the main visualization charts used for data analysis. It is easy to use both of them and helps viewers to see the results clearly. These both visualization methods are used for comparing values. As data analysts, you see your fair share of datasets. The charts are perfect when you want to compare them and similarities between these sets. They easily release the high and low values of a particular set, so you can realize huge differences or outliers. There are a few popular methods for creating a comparison chart;

Data Analysis: Most Popular Chart Types for Data visualization

Visual presentation is important part of Data analysis. Let’s take a look at all those charts step by step.

Column Chart

Tt is simple, time saving way to show the difference between different datasets. A column chart contains data labels along the horizontal (X) axis, or values presented on the vertical (Y) axis, also known as the left side of the chart. The initial point for Y axis is 0 and is as high as the largest measurement you’ve ever watched. You can use column charts to track monthly sales numbers, landing page revenue, or similar metrics. Consistent colors help the data focus on it, but you can offer accent colors to highlight important data points or see changes by the time.

Bullet Graph

Similar to previous one, these include other visual elements. When using the bullet chart, you will start with a master account and compare it with another units of measure to find a better meaning and link.

Mekko Chart

The Mekko chart has only one expectation from bar chart. Which is; The X axis measures another dimension of your data sets instead of tracking your data progress.

Pie Chart

The pie chart represents a static number divided into categories that make up its own pieces.

Bar Chart

Although bar charts limit your label and comparison area, you can generally use a bar chart and bar chart in the same way. It is best to stick to a bar chart if:

• Working with longer tags
• Viewing negative numbers
• Comparing 10 or more items

Line Chart

Line chart is designed to reveal trends, progress or changes over time. Because of that, your dataset becomes the best when it is continuous instead of being full of start and end. Like the bar chart, the data labels in the line chart are on the X axis, while the measurements are on the Y axis.

Scatter Chart

It works best when analyzing more than one data points and looking for similarities in dataset. And also, you can notice any outliers and also better understand your overall data distribution.

Descriptive Analysis for Data Analysis

Calculating descriptive analysis includes calculating mean, mode, median and standard deviation of a sample. All calculations are done and added to the “Data_Analysis.xlsx” file with their explanations.

In our database, we compare results by years. So, year is the key parameter of our calculation. However, first six years are given as years, and the later part given as divided into quarters. There are two thing we can do. We can either look at our data as 2 different datasets. First one compares by years, second one compares data by quarters. And the other thing is, we can calculate sum of the years with quarters and think them as one data. I will go with the second one. To do that, I will use the sum values in the right side of the table (K Column) in Page 1. These are the results that I found;

Mean = $1.165,91

Mode = DNE

Median = 2761,25

Standard Deviation = 2224,801482

On our dataset;

According to the dataset, in 2007 the total outstanding amount was $516 billion. In one year, it increased $61 billion. By the year passed, this kept increasing. Between 2008 and 2009, it increased another $80 billion. With this, the total change became $141 billion dollars in 2 years. In 2012, the new amount of total outstanding became $948,2 billion. It’s $432,2 billion dollars more from the data of 2007. After 2012, there became a significant jump in data. It was $948,2 billion in 2012 but $4.007,5 billion dollars in 2013. When you think of the data between 2007 and 2012, that was significant raise when we evaluate our data by years. After 2003, it continued to raise and it is still raising today, in 2020.

While reading all these numbers and years, it becomes boring and cause you to lose your focus. This is exactly the time where visual presentation is required. We need it because of a graphical summary of the data makes it easier to recognize patterns and trends rather than looking at too many of rows in a spreadsheet. This is the way how human brain works.

Since the purpose of data analysis is to have a better understanding ability, any data is more valuable when it’s viewed. Even if a data analyst can preview data without visualization, it will be more difficult to carry meaning without charts or graphics. Diagrams and charts make it easy to see the results of communication data even if you define patterns without them.

In undergraduate business schools, students learn the value of presenting data results with visualization.

Visual Presentation

Without a visual representation of the data, it can be difficult for the viewer to clutch the real meaning of the results. For example, blocking numbers doesn’t tell your boss why he should care about data, but you can be sure he’ll get his attention by showing him a graph showing how much information he can save or he can win.

Our dataset contains Federal Student Aid Portfolio Summary by years from 2007 to 2020.

According to the chart, we can clearly see that total outstanding money is increased every year. Also, there is a significant jump between 2012-2013. This chart leads us to think the increment between years and the significant jump between 2012-2013. It also leads us to think of the total improvement we made. From where to where. At the beginning, we were under $1000, after 2019, we reached out to over $60000. Now let’s change our perspective the same data with another chart.

 

After looking at this pie chart, we think based on years. Our pie chart starts by 2007 (the smallest one) and goes till 2019 (the biggest one, light blue). This chart leads us to think which year has the biggest amount between all these years.

For all the other different charts & graphics;

We can repeat this action at change our perspective by changing the chart we use. I will give it another shot.

 

After we look at this chart, we also clearly see the big jump after 2012 and the general increment by years like it is in column chart. Additionally, this chart leads us to think about how it is going to be in 2020, or future. What I am trying to say is, it leads us to forecast next years’ values. By the values of this chart, we can easily apply Linear regression to forecast next year’s value or even 2060.

Interpretation

According to the simple analysis we made in previous section, all charts have different types of effect on viewers. We can think as manipulating viewers. Where ever you want to lead them, you should pick the right chart for it. After we have seen all these charts with the same variables and same data set, we can say that column chart is useful to use for analyzing data by groups (by years in our example).

The column chart fits great for our dataset. However, if there would be too many x axis (n, years) it would be really complicated to read the chart.

When we look at the second one, pie chart, it is really easy to understand. It leads viewer to think the data as a sample of an entire dataset. In another words, you focus on one data per unit time and compare it with others and the total. The only thing is, it is not giving too many information about the dataset.

Finally, the last chart line graph, it also has the same features like column graph. Also, it leads viewers to think about the future. In another words, it leads viewers to forecast future’s data.

Recommendations

It could be really hard to visualize a dataset in our minds and make decisions depending on a thought. But when we see those data in a chart, we easily analyze them. After I wrote this assignment, I realized that different types of charts have different effects on the viewer. They all leads viewer to think something else and make viewer focus on another point. I should learn the features, pros and cons of all of them.

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